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-scale genomic and phenotypic datasets (e.g., PheWAS, statistical genetics, prediction models) Analyze high-dimensional data from biobanks and clinical information systems Contribute to teaching activities
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. The successful candidate should apply state-of-the-art methods in either aquatic ecology and biodiversity research (e.g., environmental omics, eDNA, etc.) or hydrology (e.g. integrated modeling and/or AI-based
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to cutting-edge research projects aimed at collecting massive protein fitness measurements in service of developing a new generation of predictive models linking sequence to function. Key responsibilities will
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simulations of compact binaries (including, for example, binary black holes, binary neutron stars, and black hole–neutron star binaries). The broader goals are to generate accurate predictions for gravitational
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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
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the flexibility and power of NNs with the ability of LMMs to robustly learn from structured and noisy (non i.i.d.) data, applying them on the prediction of both plants and human phenotypes. These models will
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Computational modelling of two-dimensional graphene-based materials School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Natalia Martsinovich Application Deadline
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models. The candidate will be jointly supervised by Dr. Iris Groen (www.irisgroen.com ) and Prof. Cees Snoek (https://www.ceessnoek.info/ ). Want to know more about our organisation? Read more about
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water quality parameters and predict cyanobacteria blooms in the Tietê system reservoirs. Activities: 1. Develop machine learning models for estimating water quality parameters via remote sensing; 2
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and observation models to reflect real-time changes in environmental conditions, enabling more accurate predictions of adaptation impacts and thereby supporting a better-informed, resilient decision